Supporting primary care through symptom checking artificial intelligence: a study of patient and physician attitudes in Italian general practice
Open accessAbstract
Background: Rapid advancements in artificial intelligence (AI) have led to the adoption of AI-driven symptom checkers in primary care. This study aimed to evaluate both patients' and physicians' attitudes towards these tools in Italian general practice settings, focusing on their perceived utility, user satisfaction, and potential challenges. Methods: This feasibility study involved ten general practitioners (GPs) and patients visiting GP offices. The patients used a chatbot-based symptom checker before their medical visit and conducted anamnestic screening for COVID-19 and a medical history algorithm concerning the current medical problem. The entered data were forwarded to the GP as medical history aid. After the medical visit, both physicians and patients evaluated their respective symptoms. Additionally, physicians performed a final overall evaluation of the symptom checker after the conclusion of the practice phase. Results: Most patients did not use symptom checkers. Overall, 49% of patients and 27% of physicians reported being rather or very satisfied with the symptom checker. The most frequent patient-reported reasons for satisfaction were ease of use, precise and comprehensive questions, perceived time-saving potential, and encouragement of self-reflection. Every other patient would consider at-home use of the symptom checker for the first appraisal of health problems to save time, reduce unnecessary visits, and/or as an aid for the physician. Patients’ attitudes towards the symptom checker were not significantly associated with age, sex, or level of education. Most patients (75%) and physicians (84%) indicated that the symptom checker had no effect on the duration of the medical visit. Only a few participants found the use of the symptom checker to be disruptive to the medical visit or its quality. Conclusions: The findings suggest a positive reception of the symptom checker, albeit with differing focus between patients and physicians. With the potential to be integrated further into primary care, these tools require meticulous clinical guidance to maximize their benefits. Trial registration: The study was not registered, as it did not include direct medical intervention on human participants.
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Perceived benefit, perceived usefulness, usability, ease of use, usefulness, accuracy, and reliability of the output of AI are key contributors to adoption.
“Perceived benefit, perceived usefulness, usability, ease of use, usefulness, accuracy, and reliability of the output of the AI are key contributors to adoption [19,45,62-67].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [65]
The Results (Post-visit evaluation; no page number available) link satisfaction with ease and speed of use, perceived time savings, and the adequacy of diagnostic output. The Abstract’s Results also report that some patients would consider using the symptom checker at home. The study does not establish reliability of AI output as an adoption factor or show that the listed factors are key contributors to AI adoption generally. (LLM, conf=0.85)
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- Authors
- Mahlknecht, A., Engl, A., Piccoliori, G., & Wiedermann, C. J.
- Affiliations
- Landesfachhochschule für Gesundheitsberufe Claudiana (IT)
- UMIT TIROL - Private University for Health Sciences and Health Technology (AT)
- Corresponding author
- Wiedermann, Christian Josef
- Venue
- BMC Primary Care
- Published
- 2023-09-04
- Volume
- 24
- Issue
- 1
- Pages
- 174-174
- ISSN
- 1471-2296 (ISSN-L)
- 2731-4553
- PMC
- PMC10476397
- OpenAlex
- W4386398291
- Cited by
- 41
- References
- 33
- Citations by year
- 2026: 16 · 2025: 15 · 2024: 9 · 2023: 1
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Family Practice (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- symptom checking, artificial intelligence, chatbots, primary care, general practitioners, patient attitudes, physician attitudes, user satisfaction, COVID-19 screening, telemedicine
- MeSH terms
- Artificial Intelligence, Attitude of Health Personnel, Humans, Italy, Middle Aged, Patients, Primary Health Care, General Practitioners, General Practice
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